Overview

Dataset statistics

Number of variables15
Number of observations457
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory53.7 KiB
Average record size in memory120.3 B

Variable types

Numeric14
Categorical1

Alerts

TotalSteps is highly correlated with TotalDistance and 8 other fieldsHigh correlation
TotalDistance is highly correlated with TotalSteps and 8 other fieldsHigh correlation
TrackerDistance is highly correlated with TotalSteps and 8 other fieldsHigh correlation
VeryActiveDistance is highly correlated with TotalSteps and 5 other fieldsHigh correlation
ModeratelyActiveDistance is highly correlated with TotalSteps and 6 other fieldsHigh correlation
LightActiveDistance is highly correlated with TotalSteps and 5 other fieldsHigh correlation
VeryActiveMinutes is highly correlated with TotalSteps and 5 other fieldsHigh correlation
FairlyActiveMinutes is highly correlated with TotalSteps and 6 other fieldsHigh correlation
LightlyActiveMinutes is highly correlated with TotalSteps and 3 other fieldsHigh correlation
Calories is highly correlated with TotalSteps and 2 other fieldsHigh correlation
TotalSteps is highly correlated with TotalDistance and 7 other fieldsHigh correlation
TotalDistance is highly correlated with TotalSteps and 7 other fieldsHigh correlation
TrackerDistance is highly correlated with TotalSteps and 7 other fieldsHigh correlation
VeryActiveDistance is highly correlated with TotalSteps and 3 other fieldsHigh correlation
ModeratelyActiveDistance is highly correlated with TotalSteps and 2 other fieldsHigh correlation
LightActiveDistance is highly correlated with TotalSteps and 3 other fieldsHigh correlation
VeryActiveMinutes is highly correlated with TotalSteps and 4 other fieldsHigh correlation
LightlyActiveMinutes is highly correlated with TotalSteps and 3 other fieldsHigh correlation
Calories is highly correlated with TotalSteps and 3 other fieldsHigh correlation
Id is highly correlated with LoggedActivitiesDistance and 1 other fieldsHigh correlation
TotalSteps is highly correlated with TotalDistance and 5 other fieldsHigh correlation
TotalDistance is highly correlated with TotalSteps and 5 other fieldsHigh correlation
TrackerDistance is highly correlated with TotalSteps and 5 other fieldsHigh correlation
LoggedActivitiesDistance is highly correlated with Id and 4 other fieldsHigh correlation
VeryActiveDistance is highly correlated with SedentaryActiveDistanceHigh correlation
ModeratelyActiveDistance is highly correlated with SedentaryActiveDistanceHigh correlation
LightActiveDistance is highly correlated with TotalSteps and 4 other fieldsHigh correlation
SedentaryActiveDistance is highly correlated with Id and 7 other fieldsHigh correlation
LightlyActiveMinutes is highly correlated with TotalSteps and 3 other fieldsHigh correlation
Id is highly correlated with TotalSteps and 6 other fieldsHigh correlation
ActivityDate is highly correlated with CaloriesHigh correlation
TotalSteps is highly correlated with Id and 9 other fieldsHigh correlation
TotalDistance is highly correlated with Id and 9 other fieldsHigh correlation
TrackerDistance is highly correlated with Id and 9 other fieldsHigh correlation
LoggedActivitiesDistance is highly correlated with SedentaryActiveDistance and 1 other fieldsHigh correlation
VeryActiveDistance is highly correlated with Id and 5 other fieldsHigh correlation
ModeratelyActiveDistance is highly correlated with TotalSteps and 3 other fieldsHigh correlation
LightActiveDistance is highly correlated with TotalSteps and 5 other fieldsHigh correlation
SedentaryActiveDistance is highly correlated with LoggedActivitiesDistanceHigh correlation
VeryActiveMinutes is highly correlated with Id and 4 other fieldsHigh correlation
FairlyActiveMinutes is highly correlated with ModeratelyActiveDistanceHigh correlation
LightlyActiveMinutes is highly correlated with Id and 7 other fieldsHigh correlation
SedentaryMinutes is highly correlated with TotalSteps and 5 other fieldsHigh correlation
Calories is highly correlated with Id and 6 other fieldsHigh correlation
TotalSteps has 61 (13.3%) zeros Zeros
TotalDistance has 63 (13.8%) zeros Zeros
TrackerDistance has 66 (14.4%) zeros Zeros
LoggedActivitiesDistance has 433 (94.7%) zeros Zeros
VeryActiveDistance has 245 (53.6%) zeros Zeros
ModeratelyActiveDistance has 228 (49.9%) zeros Zeros
LightActiveDistance has 74 (16.2%) zeros Zeros
SedentaryActiveDistance has 419 (91.7%) zeros Zeros
VeryActiveMinutes has 241 (52.7%) zeros Zeros
FairlyActiveMinutes has 227 (49.7%) zeros Zeros
LightlyActiveMinutes has 72 (15.8%) zeros Zeros
Calories has 5 (1.1%) zeros Zeros

Reproduction

Analysis started2022-02-01 05:33:23.802776
Analysis finished2022-02-01 05:33:47.331453
Duration23.53 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct35
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4628594643
Minimum1503960366
Maximum8877689391
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2022-02-01T11:03:47.463456image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1503960366
5-th percentile1624580081
Q12347167796
median4057192912
Q36391747486
95-th percentile8792009665
Maximum8877689391
Range7373729025
Interquartile range (IQR)4044579690

Descriptive statistics

Standard deviation2293781430
Coefficient of variation (CV)0.4955675764
Kurtosis-1.039194515
Mean4628594643
Median Absolute Deviation (MAD)2030840877
Skewness0.3527246238
Sum2.115267752 × 1012
Variance5.261433247 × 1018
MonotonicityIncreasing
2022-02-01T11:03:47.569427image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
405719291232
 
7.0%
402033265032
 
7.0%
150396036619
 
4.2%
162458008119
 
4.2%
234716779615
 
3.3%
470292168415
 
3.3%
444511498615
 
3.3%
696218106714
 
3.1%
232012700212
 
2.6%
455860992412
 
2.6%
Other values (25)272
59.5%
ValueCountFrequency (%)
150396036619
4.2%
162458008119
4.2%
164443008110
2.2%
184450507212
2.6%
192797227912
2.6%
202248440812
2.6%
202635203512
2.6%
232012700212
2.6%
234716779615
3.3%
287321276512
2.6%
ValueCountFrequency (%)
887768939112
2.6%
879200966512
2.6%
85838150598
1.8%
837856320012
2.6%
825324287912
2.6%
805347532811
2.4%
708636192612
2.6%
700774417112
2.6%
696218106714
3.1%
67758889559
2.0%

ActivityDate
Categorical

HIGH CORRELATION

Distinct32
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
4/2/2016
35 
4/3/2016
35 
4/4/2016
35 
4/5/2016
35 
4/1/2016
34 
Other values (27)
283 

Length

Max length9
Median length8
Mean length8.332603939
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3/25/2016
2nd row3/26/2016
3rd row3/27/2016
4th row3/28/2016
5th row3/29/2016

Common Values

ValueCountFrequency (%)
4/2/201635
 
7.7%
4/3/201635
 
7.7%
4/4/201635
 
7.7%
4/5/201635
 
7.7%
4/1/201634
 
7.4%
4/6/201633
 
7.2%
4/7/201633
 
7.2%
4/8/201633
 
7.2%
4/9/201632
 
7.0%
4/10/201629
 
6.3%
Other values (22)123
26.9%

Length

2022-02-01T11:03:47.674461image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4/2/201635
 
7.7%
4/5/201635
 
7.7%
4/3/201635
 
7.7%
4/4/201635
 
7.7%
4/1/201634
 
7.4%
4/6/201633
 
7.2%
4/7/201633
 
7.2%
4/8/201633
 
7.2%
4/9/201632
 
7.0%
4/10/201629
 
6.3%
Other values (22)123
26.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

TotalSteps
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct389
Distinct (%)85.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6546.562363
Minimum0
Maximum28497
Zeros61
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2022-02-01T11:03:47.771848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11988
median5986
Q310198
95-th percentile15605.6
Maximum28497
Range28497
Interquartile range (IQR)8210

Descriptive statistics

Standard deviation5398.493064
Coefficient of variation (CV)0.824630205
Kurtosis0.6648241126
Mean6546.562363
Median Absolute Deviation (MAD)4120
Skewness0.803413395
Sum2991779
Variance29143727.36
MonotonicityNot monotonic
2022-02-01T11:03:47.885818image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
061
 
13.3%
20982
 
0.4%
82
 
0.4%
55432
 
0.4%
124092
 
0.4%
44992
 
0.4%
63442
 
0.4%
41952
 
0.4%
72
 
0.4%
19881
 
0.2%
Other values (379)379
82.9%
ValueCountFrequency (%)
061
13.3%
41
 
0.2%
72
 
0.4%
82
 
0.4%
141
 
0.2%
181
 
0.2%
201
 
0.2%
241
 
0.2%
441
 
0.2%
871
 
0.2%
ValueCountFrequency (%)
284971
0.2%
275721
0.2%
257011
0.2%
241361
0.2%
230141
0.2%
207791
0.2%
202371
0.2%
201881
0.2%
196581
0.2%
189521
0.2%

TotalDistance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct334
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.663522972
Minimum0
Maximum27.53000069
Zeros63
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2022-02-01T11:03:48.005819image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.409999967
median4.090000153
Q37.159999847
95-th percentile11.23999977
Maximum27.53000069
Range27.53000069
Interquartile range (IQR)5.749999881

Descriptive statistics

Standard deviation4.082072268
Coefficient of variation (CV)0.8753194296
Kurtosis3.448976394
Mean4.663522972
Median Absolute Deviation (MAD)2.889999866
Skewness1.321383526
Sum2131.229998
Variance16.663314
MonotonicityNot monotonic
2022-02-01T11:03:48.203439image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
063
 
13.8%
0.0099999997766
 
1.3%
4.719999793
 
0.7%
7.6700000763
 
0.7%
5.4099998472
 
0.4%
2.1400001052
 
0.4%
3.8399999142
 
0.4%
0.019999999552
 
0.4%
0.25999999052
 
0.4%
1.5499999522
 
0.4%
Other values (324)370
81.0%
ValueCountFrequency (%)
063
13.8%
0.0099999997766
 
1.3%
0.019999999552
 
0.4%
0.029999999331
 
0.2%
0.10999999941
 
0.2%
0.12999999521
 
0.2%
0.14000000062
 
0.4%
0.18999999761
 
0.2%
0.20999999341
 
0.2%
0.25999999052
 
0.4%
ValueCountFrequency (%)
27.530000691
0.2%
23.389999391
0.2%
20.909999851
0.2%
20.389999391
0.2%
20.139999391
0.2%
18.409999851
0.2%
15.819999691
0.2%
15.619999891
0.2%
14.840000151
0.2%
14.710000041
0.2%

TrackerDistance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct336
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.609846824
Minimum0
Maximum27.53000069
Zeros66
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2022-02-01T11:03:48.320410image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.279999971
median4.090000153
Q37.110000134
95-th percentile11.13599968
Maximum27.53000069
Range27.53000069
Interquartile range (IQR)5.830000162

Descriptive statistics

Standard deviation4.068539937
Coefficient of variation (CV)0.8825759494
Kurtosis3.576888152
Mean4.609846824
Median Absolute Deviation (MAD)2.920000076
Skewness1.339050019
Sum2106.699999
Variance16.55301722
MonotonicityNot monotonic
2022-02-01T11:03:48.430438image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
066
 
14.4%
0.0099999997766
 
1.3%
4.719999793
 
0.7%
7.6700000763
 
0.7%
6.7899999622
 
0.4%
6.1500000952
 
0.4%
11.239999772
 
0.4%
1.9199999572
 
0.4%
4.1399998662
 
0.4%
1.4299999482
 
0.4%
Other values (326)367
80.3%
ValueCountFrequency (%)
066
14.4%
0.0099999997766
 
1.3%
0.019999999552
 
0.4%
0.029999999331
 
0.2%
0.10999999941
 
0.2%
0.12999999521
 
0.2%
0.14000000062
 
0.4%
0.18999999761
 
0.2%
0.20999999341
 
0.2%
0.25999999052
 
0.4%
ValueCountFrequency (%)
27.530000691
0.2%
23.389999391
0.2%
20.909999851
0.2%
20.389999391
0.2%
20.139999391
0.2%
18.409999851
0.2%
15.819999691
0.2%
15.619999891
0.2%
14.840000151
0.2%
14.710000041
0.2%

LoggedActivitiesDistance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct20
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1794273741
Minimum0
Maximum6.72705698
Zeros433
Zeros (%)94.7%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2022-02-01T11:03:48.528439image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.3665432006
Maximum6.72705698
Range6.72705698
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8492318298
Coefficient of variation (CV)4.733011526
Kurtosis27.13615724
Mean0.1794273741
Median Absolute Deviation (MAD)0
Skewness5.159788142
Sum81.99830997
Variance0.7211947008
MonotonicityNot monotonic
2022-02-01T11:03:48.613439image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0433
94.7%
2.0921471124
 
0.9%
2.2530810833
 
0.7%
1.6093440061
 
0.2%
5.1898498541
 
0.2%
3.2186880111
 
0.2%
4.8697829251
 
0.2%
4.8423199651
 
0.2%
4.8759899141
 
0.2%
4.8357200621
 
0.2%
Other values (10)10
 
2.2%
ValueCountFrequency (%)
0433
94.7%
0.055842999371
 
0.2%
1.6093440061
 
0.2%
1.9263019561
 
0.2%
2.0277729031
 
0.2%
2.0921471124
 
0.9%
2.2530810833
 
0.7%
2.6964550021
 
0.2%
3.2186880111
 
0.2%
3.972795011
 
0.2%
ValueCountFrequency (%)
6.727056981
0.2%
5.456863881
0.2%
5.1898498541
0.2%
4.9012827871
0.2%
4.8759899141
0.2%
4.8697829251
0.2%
4.8423199651
0.2%
4.8363800051
0.2%
4.8357200621
0.2%
4.8280320171
0.2%

VeryActiveDistance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct170
Distinct (%)37.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.180897153
Minimum0
Maximum21.92000008
Zeros245
Zeros (%)53.6%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2022-02-01T11:03:48.722407image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.309999943
95-th percentile5.760000038
Maximum21.92000008
Range21.92000008
Interquartile range (IQR)1.309999943

Descriptive statistics

Standard deviation2.487158568
Coefficient of variation (CV)2.106160186
Kurtosis18.7096635
Mean1.180897153
Median Absolute Deviation (MAD)0
Skewness3.73064421
Sum539.669999
Variance6.185957745
MonotonicityNot monotonic
2022-02-01T11:03:48.836442image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0245
53.6%
0.07000000036
 
1.3%
0.254
 
0.9%
0.33000001313
 
0.7%
0.23000000423
 
0.7%
0.53
 
0.7%
2.5699999332
 
0.4%
0.079999998212
 
0.4%
0.059999998662
 
0.4%
0.88999998572
 
0.4%
Other values (160)185
40.5%
ValueCountFrequency (%)
0245
53.6%
0.0099999997761
 
0.2%
0.019999999551
 
0.2%
0.039999999111
 
0.2%
0.059999998662
 
0.4%
0.07000000036
 
1.3%
0.079999998212
 
0.4%
0.090000003581
 
0.2%
0.10000000152
 
0.4%
0.10999999941
 
0.2%
ValueCountFrequency (%)
21.920000081
0.2%
16.819999691
0.2%
14.720000271
0.2%
12.220000271
0.2%
12.060000421
0.2%
11.729999541
0.2%
11.100000381
0.2%
9.9799995421
0.2%
9.9700002671
0.2%
9.9600000381
0.2%

ModeratelyActiveDistance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct140
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4786433252
Minimum0
Maximum6.400000095
Zeros228
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2022-02-01T11:03:48.952409image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.01999999955
Q30.6700000167
95-th percentile2.089999914
Maximum6.400000095
Range6.400000095
Interquartile range (IQR)0.6700000167

Descriptive statistics

Standard deviation0.8309951707
Coefficient of variation (CV)1.736146995
Kurtosis11.87056988
Mean0.4786433252
Median Absolute Deviation (MAD)0.01999999955
Skewness2.971412313
Sum218.7399996
Variance0.6905529737
MonotonicityNot monotonic
2022-02-01T11:03:49.067410image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0228
49.9%
0.18000000725
 
1.1%
0.255
 
1.1%
0.33000001314
 
0.9%
0.25999999054
 
0.9%
0.23000000424
 
0.9%
0.37000000484
 
0.9%
0.52999997144
 
0.9%
0.21999999884
 
0.9%
0.79000002154
 
0.9%
Other values (130)191
41.8%
ValueCountFrequency (%)
0228
49.9%
0.019999999551
 
0.2%
0.039999999112
 
0.4%
0.050000000753
 
0.7%
0.059999998661
 
0.2%
0.090000003581
 
0.2%
0.10000000151
 
0.2%
0.11999999732
 
0.4%
0.12999999521
 
0.2%
0.14000000061
 
0.2%
ValueCountFrequency (%)
6.4000000951
0.2%
5.4899997711
0.2%
4.4899997711
0.2%
4.4400000571
0.2%
3.7200000291
0.2%
3.6800000671
0.2%
3.5999999051
0.2%
3.3399999141
0.2%
3.259999991
0.2%
3.1199998861
0.2%

LightActiveDistance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct295
Distinct (%)64.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.890196937
Minimum0
Maximum12.51000023
Zeros74
Zeros (%)16.2%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2022-02-01T11:03:49.182410image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.8700000048
median2.930000067
Q34.460000038
95-th percentile6.515999985
Maximum12.51000023
Range12.51000023
Interquartile range (IQR)3.590000033

Descriptive statistics

Standard deviation2.237523344
Coefficient of variation (CV)0.7741767752
Kurtosis0.2750757813
Mean2.890196937
Median Absolute Deviation (MAD)1.749999762
Skewness0.5215831453
Sum1320.82
Variance5.006510715
MonotonicityNot monotonic
2022-02-01T11:03:49.302410image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
074
 
16.2%
0.0099999997767
 
1.5%
3.54
 
0.9%
4.6100001344
 
0.9%
4.4499998093
 
0.7%
3.2100000383
 
0.7%
3.9100000863
 
0.7%
4.6799998283
 
0.7%
4.4600000383
 
0.7%
5.2699999813
 
0.7%
Other values (285)350
76.6%
ValueCountFrequency (%)
074
16.2%
0.0099999997767
 
1.5%
0.019999999551
 
0.2%
0.029999999331
 
0.2%
0.10999999941
 
0.2%
0.12999999522
 
0.4%
0.14000000061
 
0.2%
0.17000000181
 
0.2%
0.18999999761
 
0.2%
0.20999999342
 
0.4%
ValueCountFrequency (%)
12.510000231
0.2%
121
0.2%
9.3699998861
0.2%
8.6199998861
0.2%
8.1499996191
0.2%
8.0799999241
0.2%
8.060000421
0.2%
8.0200004581
0.2%
7.7899999621
0.2%
7.5599999431
0.2%

SedentaryActiveDistance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.001903719882
Minimum0
Maximum0.1000000015
Zeros419
Zeros (%)91.7%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2022-02-01T11:03:49.398410image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.009999999776
Maximum0.1000000015
Range0.1000000015
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0084868013
Coefficient of variation (CV)4.45800949
Kurtosis54.03881246
Mean0.001903719882
Median Absolute Deviation (MAD)0
Skewness6.566301549
Sum0.8699999861
Variance7.202579631 × 10-5
MonotonicityNot monotonic
2022-02-01T11:03:49.479410image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0419
91.7%
0.00999999977622
 
4.8%
0.029999999336
 
1.3%
0.039999999114
 
0.9%
0.019999999552
 
0.4%
0.059999998662
 
0.4%
0.10000000151
 
0.2%
0.050000000751
 
0.2%
ValueCountFrequency (%)
0419
91.7%
0.00999999977622
 
4.8%
0.019999999552
 
0.4%
0.029999999336
 
1.3%
0.039999999114
 
0.9%
0.050000000751
 
0.2%
0.059999998662
 
0.4%
0.10000000151
 
0.2%
ValueCountFrequency (%)
0.10000000151
 
0.2%
0.059999998662
 
0.4%
0.050000000751
 
0.2%
0.039999999114
 
0.9%
0.029999999336
 
1.3%
0.019999999552
 
0.4%
0.00999999977622
 
4.8%
0419
91.7%

VeryActiveMinutes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct85
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.62363239
Minimum0
Maximum202
Zeros241
Zeros (%)52.7%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2022-02-01T11:03:49.580438image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q325
95-th percentile78.4
Maximum202
Range202
Interquartile range (IQR)25

Descriptive statistics

Standard deviation28.91970375
Coefficient of variation (CV)1.739674163
Kurtosis6.928585794
Mean16.62363239
Median Absolute Deviation (MAD)0
Skewness2.38473603
Sum7597
Variance836.3492648
MonotonicityNot monotonic
2022-02-01T11:03:49.700440image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0241
52.7%
111
 
2.4%
39
 
2.0%
48
 
1.8%
27
 
1.5%
187
 
1.5%
157
 
1.5%
77
 
1.5%
256
 
1.3%
56
 
1.3%
Other values (75)148
32.4%
ValueCountFrequency (%)
0241
52.7%
111
 
2.4%
27
 
1.5%
39
 
2.0%
48
 
1.8%
56
 
1.3%
63
 
0.7%
77
 
1.5%
84
 
0.9%
94
 
0.9%
ValueCountFrequency (%)
2021
0.2%
1651
0.2%
1281
0.2%
1241
0.2%
1231
0.2%
1161
0.2%
1131
0.2%
1071
0.2%
1061
0.2%
1051
0.2%

FairlyActiveMinutes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct62
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.07002188
Minimum0
Maximum660
Zeros227
Zeros (%)49.7%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2022-02-01T11:03:49.904651image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q316
95-th percentile46.4
Maximum660
Range660
Interquartile range (IQR)16

Descriptive statistics

Standard deviation36.20863518
Coefficient of variation (CV)2.770357656
Kurtosis224.7286191
Mean13.07002188
Median Absolute Deviation (MAD)1
Skewness13.02940846
Sum5973
Variance1311.065262
MonotonicityNot monotonic
2022-02-01T11:03:50.020681image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0227
49.7%
614
 
3.1%
1612
 
2.6%
811
 
2.4%
710
 
2.2%
119
 
2.0%
129
 
2.0%
179
 
2.0%
158
 
1.8%
98
 
1.8%
Other values (52)140
30.6%
ValueCountFrequency (%)
0227
49.7%
13
 
0.7%
23
 
0.7%
33
 
0.7%
47
 
1.5%
55
 
1.1%
614
 
3.1%
710
 
2.2%
811
 
2.4%
98
 
1.8%
ValueCountFrequency (%)
6601
0.2%
1411
0.2%
1331
0.2%
1201
0.2%
1141
0.2%
1071
0.2%
1011
0.2%
991
0.2%
812
0.4%
771
0.2%

LightlyActiveMinutes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct251
Distinct (%)54.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean170.0700219
Minimum0
Maximum720
Zeros72
Zeros (%)15.8%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2022-02-01T11:03:50.139680image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q164
median181
Q3257
95-th percentile348.6
Maximum720
Range720
Interquartile range (IQR)193

Descriptive statistics

Standard deviation122.2053721
Coefficient of variation (CV)0.7185591605
Kurtosis0.3822726388
Mean170.0700219
Median Absolute Deviation (MAD)90
Skewness0.3525141968
Sum77722
Variance14934.15298
MonotonicityNot monotonic
2022-02-01T11:03:50.252675image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
072
 
15.8%
16
 
1.3%
2485
 
1.1%
2304
 
0.9%
2124
 
0.9%
2764
 
0.9%
2084
 
0.9%
1533
 
0.7%
1763
 
0.7%
2413
 
0.7%
Other values (241)349
76.4%
ValueCountFrequency (%)
072
15.8%
16
 
1.3%
23
 
0.7%
31
 
0.2%
61
 
0.2%
82
 
0.4%
91
 
0.2%
111
 
0.2%
121
 
0.2%
141
 
0.2%
ValueCountFrequency (%)
7201
0.2%
6301
0.2%
5861
0.2%
5061
0.2%
4911
0.2%
4751
0.2%
4221
0.2%
4011
0.2%
3971
0.2%
3901
0.2%

SedentaryMinutes
Real number (ℝ≥0)

HIGH CORRELATION

Distinct315
Distinct (%)68.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean995.2822757
Minimum32
Maximum1440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2022-02-01T11:03:50.371652image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile465.4
Q1728
median1057
Q31285
95-th percentile1440
Maximum1440
Range1408
Interquartile range (IQR)557

Descriptive statistics

Standard deviation337.021404
Coefficient of variation (CV)0.3386189146
Kurtosis-0.6782227116
Mean995.2822757
Median Absolute Deviation (MAD)300
Skewness-0.3655631139
Sum454844
Variance113583.4267
MonotonicityNot monotonic
2022-02-01T11:03:50.490676image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
144063
 
13.8%
11814
 
0.9%
14394
 
0.9%
11253
 
0.7%
11503
 
0.7%
8423
 
0.7%
11393
 
0.7%
7003
 
0.7%
10553
 
0.7%
13283
 
0.7%
Other values (305)365
79.9%
ValueCountFrequency (%)
321
0.2%
611
0.2%
751
0.2%
991
0.2%
1461
0.2%
1611
0.2%
1871
0.2%
1981
0.2%
2071
0.2%
2091
0.2%
ValueCountFrequency (%)
144063
13.8%
14394
 
0.9%
14382
 
0.4%
14321
 
0.2%
14281
 
0.2%
14141
 
0.2%
14071
 
0.2%
14062
 
0.4%
14041
 
0.2%
13991
 
0.2%

Calories
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct383
Distinct (%)83.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2189.452954
Minimum0
Maximum4562
Zeros5
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2022-02-01T11:03:50.610651image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile790.8
Q11776
median2062
Q32667
95-th percentile3716
Maximum4562
Range4562
Interquartile range (IQR)891

Descriptive statistics

Standard deviation815.4845229
Coefficient of variation (CV)0.3724603999
Kurtosis0.5200100199
Mean2189.452954
Median Absolute Deviation (MAD)422
Skewness0.2363575209
Sum1000580
Variance665015.0071
MonotonicityNot monotonic
2022-02-01T11:03:50.730651image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
177611
 
2.4%
18788
 
1.8%
20606
 
1.3%
18205
 
1.1%
05
 
1.1%
19205
 
1.1%
14295
 
1.1%
13244
 
0.9%
21883
 
0.7%
19353
 
0.7%
Other values (373)402
88.0%
ValueCountFrequency (%)
05
1.1%
501
 
0.2%
1821
 
0.2%
2511
 
0.2%
3992
 
0.4%
4461
 
0.2%
4891
 
0.2%
5381
 
0.2%
6001
 
0.2%
6251
 
0.2%
ValueCountFrequency (%)
45621
0.2%
45261
0.2%
44301
0.2%
42861
0.2%
42341
0.2%
42201
0.2%
41961
0.2%
41281
0.2%
40391
0.2%
40341
0.2%

Interactions

2022-02-01T11:03:45.392276image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:26.737217image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:28.175004image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:29.616641image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:30.955020image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:32.380091image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:33.876758image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:35.238761image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:36.743396image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:38.115142image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:39.579142image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:41.106817image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:42.478966image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:43.980251image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:45.487249image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:26.857218image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:28.273973image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:29.717015image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:31.053013image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:32.484062image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:33.973758image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:35.351758image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:36.844396image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:38.215142image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:39.684573image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:41.208817image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:42.587965image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:44.083249image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:45.577279image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:26.953251image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:28.368973image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:29.809984image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:31.144017image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:32.584060image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:34.064758image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:35.452759image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:36.943395image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:38.310142image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:39.783786image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:41.302812image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:42.685996image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:44.181281image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:45.756454image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:27.049247image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:28.461973image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:29.900984image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:31.234985image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:32.680092image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:34.156758image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:35.557760image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:37.037426image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:38.405142image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:39.884788image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:41.395786image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:42.784966image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:44.279250image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:45.844456image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:27.145216image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:28.558973image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:29.993015image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:31.412016image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:32.777060image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:34.249758image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:35.659427image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:37.131396image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:38.586142image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:39.983786image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:41.488817image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:42.882966image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:44.376249image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:45.942451image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:27.254217image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:28.659977image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:30.091015image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:31.510015image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:32.879061image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:34.348758image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:35.761422image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:37.231398image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:38.688142image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:40.089787image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:41.590817image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:42.987965image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:44.480250image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:46.027456image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:27.359216image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:28.750666image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:30.179984image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:31.599986image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:32.974060image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:34.448760image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:35.853398image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:37.323398image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:38.784140image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:40.184787image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:41.684997image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:43.083574image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:44.575248image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:46.119457image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:27.461219image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:28.848642image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:30.274986image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:31.698062image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:33.074635image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:34.547759image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:35.957397image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:37.421398image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:38.884142image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:40.287788image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:41.783966image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:43.184574image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:44.677279image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:46.212428image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:27.565218image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:29.030670image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:30.370984image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:31.794062image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:33.174633image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:34.645760image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:36.054426image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:37.519399image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:38.981140image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:40.389818image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:41.881966image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:43.368572image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:44.778250image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:46.303457image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:27.665375image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:29.125668image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:30.466986image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:31.891062image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:33.275633image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:34.740761image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:36.237398image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:37.616398image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:39.079143image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:40.492787image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:41.979967image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:43.468575image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:44.878250image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:46.400460image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:27.775373image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:29.229640image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:30.568986image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:31.993060image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:33.384635image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:34.843760image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:36.341395image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:37.720596image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:39.184172image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:40.600787image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:42.084999image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:43.575546image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:44.986250image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:46.492453image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:27.875373image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:29.326670image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:30.664986image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:32.092061image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:33.485664image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:34.940761image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:36.440427image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:37.818566image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:39.284140image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:40.704786image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:42.182966image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:43.677544image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:45.087250image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:46.587456image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:27.980371image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:29.426670image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:30.764986image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:32.192061image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:33.591664image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:35.040761image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:36.548398image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:37.920566image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:39.386141image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:40.813820image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:42.284965image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:43.782279image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:45.193250image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:46.682450image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:28.082999image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:29.526670image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:30.865017image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:32.291062image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:33.783758image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:35.143760image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:36.651422image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:38.022565image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:39.487142image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:41.003789image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:42.386966image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:43.886279image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T11:03:45.298249image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2022-02-01T11:03:50.843651image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-01T11:03:51.045683image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-01T11:03:51.243680image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-01T11:03:51.442675image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-01T11:03:46.866457image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-01T11:03:47.118459image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

IdActivityDateTotalStepsTotalDistanceTrackerDistanceLoggedActivitiesDistanceVeryActiveDistanceModeratelyActiveDistanceLightActiveDistanceSedentaryActiveDistanceVeryActiveMinutesFairlyActiveMinutesLightlyActiveMinutesSedentaryMinutesCalories
015039603663/25/2016110047.117.110.02.570.464.070.033122058041819
115039603663/26/20161760911.5511.550.06.920.733.910.089172745882154
215039603663/27/2016127368.538.530.04.660.163.710.05652686051944
315039603663/28/2016132318.938.930.03.190.794.950.0392022410801932
415039603663/29/2016120417.857.850.02.161.094.610.028282437631886
515039603663/30/2016109707.167.160.02.360.514.290.0301322311741820
615039603663/31/2016122567.867.860.02.290.495.040.033122398201889
715039603664/1/2016122627.877.870.03.320.833.640.047212008661868
815039603664/2/2016112487.257.250.03.000.453.740.040112446361843
915039603664/3/2016100166.376.370.00.911.284.180.015303146551850

Last rows

IdActivityDateTotalStepsTotalDistanceTrackerDistanceLoggedActivitiesDistanceVeryActiveDistanceModeratelyActiveDistanceLightActiveDistanceSedentaryActiveDistanceVeryActiveMinutesFairlyActiveMinutesLightlyActiveMinutesSedentaryMinutesCalories
44788776893914/3/2016152608.1900008.1900000.01.800.755.570.001061725910583864
44888776893914/4/20162077918.41000018.4100000.011.730.656.000.00781620811383662
44988776893914/5/2016106958.1200008.1200000.00.770.187.090.0110324611812834
45088776893914/6/20162413620.91000020.9100000.012.220.548.080.00871631810194039
45188776893914/7/2016109108.4200008.4200000.02.960.395.030.00321121211852947
45288776893914/8/20162301420.38999920.3899990.011.100.638.620.0070293599824196
45388776893914/9/2016164708.0700008.0700000.00.000.028.020.0090928910523841
45488776893914/10/20162849727.53000127.5300010.021.921.124.460.001284621110554526
45588776893914/11/2016106228.0600008.0600000.01.470.156.370.0118722511902820
45688776893914/12/201623501.7800001.7800000.00.000.001.780.000058531938